Implicit Argument Prediction as Reading Comprehension

  • Pengxiang Cheng The University of Texas at Austin
  • Katrin Erk The University of Texas at Austin


Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples. We present a new model for implicit argument prediction that draws on reading comprehension, casting the predicate-argument tuple with the missing argument as a query. We also draw on pointer networks and multi-hop computation. Our model shows good performance on an argument cloze task as well as on a nominal implicit argument prediction task.

AAAI Technical Track: Natural Language Processing